Overview

Dataset statistics

Number of variables49
Number of observations746498
Missing cells7533934
Missing cells (%)20.6%
Total size in memory279.1 MiB
Average record size in memory392.0 B

Variable types

Text32
Numeric17

Alerts

INJURIES_UNKNOWN has constant value ""Constant
CRASH_DATE_EST_I has 690109 (92.4%) missing valuesMissing
LANE_CNT has 547494 (73.3%) missing valuesMissing
REPORT_TYPE has 21222 (2.8%) missing valuesMissing
INTERSECTION_RELATED_I has 575368 (77.1%) missing valuesMissing
NOT_RIGHT_OF_WAY_I has 711724 (95.3%) missing valuesMissing
HIT_AND_RUN_I has 513706 (68.8%) missing valuesMissing
PHOTOS_TAKEN_I has 737007 (98.7%) missing valuesMissing
STATEMENTS_TAKEN_I has 730402 (97.8%) missing valuesMissing
DOORING_I has 744212 (99.7%) missing valuesMissing
WORK_ZONE_I has 742175 (99.4%) missing valuesMissing
WORK_ZONE_TYPE has 743128 (99.5%) missing valuesMissing
WORKERS_PRESENT_I has 745383 (99.9%) missing valuesMissing
LANE_CNT is highly skewed (γ1 = 350.2991961)Skewed
INJURIES_FATAL is highly skewed (γ1 = 36.80343103)Skewed
LATITUDE is highly skewed (γ1 = -117.0740476)Skewed
LONGITUDE is highly skewed (γ1 = 128.3392678)Skewed
CRASH_RECORD_ID has unique valuesUnique
LANE_CNT has 8032 (1.1%) zerosZeros
INJURIES_TOTAL has 643471 (86.2%) zerosZeros
INJURIES_FATAL has 744067 (99.7%) zerosZeros
INJURIES_INCAPACITATING has 732026 (98.1%) zerosZeros
INJURIES_NON_INCAPACITATING has 685250 (91.8%) zerosZeros
INJURIES_REPORTED_NOT_EVIDENT has 710835 (95.2%) zerosZeros
INJURIES_NO_INDICATION has 15356 (2.1%) zerosZeros
INJURIES_UNKNOWN has 744879 (99.8%) zerosZeros
CRASH_HOUR has 16136 (2.2%) zerosZeros

Reproduction

Analysis started2023-11-18 13:41:03.196117
Analysis finished2023-11-18 13:42:14.434388
Duration1 minute and 11.24 seconds
Software versionydata-profiling vv4.6.1
Download configurationconfig.json

Variables

CRASH_RECORD_ID
Text

UNIQUE 

Distinct746498
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size5.7 MiB
2023-11-18T13:42:15.453836image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Length

Max length128
Median length128
Mean length128
Min length128

Characters and Unicode

Total characters95551744
Distinct characters16
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique746498 ?
Unique (%)100.0%

Sample

1st row012c5bfce715efb2f2b387d6dd86f9c13e9dc1809fb52ac71501da6110fbd95848a4f380e1b553f8dc03c70f782e85adb155ab97cbeabef83ba76d390c21f535
2nd row01d457f032e23d935a0b8f6b4c88221375180ffd4cd959492eea49079953f5168d9dd08cba3d0061a8611e8c1115d2d7b83f39578025012b5f286671e7bcf4ec
3rd row02249b4747a4bf40b88a8357304a98dfeaef9c38eebbf00c3fbedb50b61241515d2d1f4461f23962841f95438429b5cd9e5c0cdacc1d224cad0b2876ce330d33
4th row03e3b6caad71b78ed9ae325648effa9512bfb2517aed3054e396c283dcfe869598b1a63db575c612fa4205c9b49f515c756cef13d72dcdcc3ef12993874c407a
5th row0481fc919b38f1572d4ba04b069766102d904a662ff0969f04266ebc48a9605d625c64356bd669d7bc1b778f5500a140812b58f19f2044372e50b31a01f030d4
ValueCountFrequency (%)
012c5bfce715efb2f2b387d6dd86f9c13e9dc1809fb52ac71501da6110fbd95848a4f380e1b553f8dc03c70f782e85adb155ab97cbeabef83ba76d390c21f535 1
 
< 0.1%
14ea736b720b0901daea5d981f5a1ecb669b8dab9b6cad5e665497eaa1e229d010454aabb5e87e34061874c3c3e5ca994c165276b4f980d19a3282a9ec08aaa8 1
 
< 0.1%
457761517b80a1121f4c5a726e39e3ec0313d8fe41341d029dc01b4656281c2e4acb6f95829bceafd67289a3e2f276986a182d6d6386570852fcfd6f15791018 1
 
< 0.1%
11d15c6567af29ebf6b948be4e66507cf2d6e8730377f388875550d3fd95cfb5f48b13128ef36895bef0483c2c813550d3816c8a5e865ed0e6365b5e1444ad2a 1
 
< 0.1%
02249b4747a4bf40b88a8357304a98dfeaef9c38eebbf00c3fbedb50b61241515d2d1f4461f23962841f95438429b5cd9e5c0cdacc1d224cad0b2876ce330d33 1
 
< 0.1%
03e3b6caad71b78ed9ae325648effa9512bfb2517aed3054e396c283dcfe869598b1a63db575c612fa4205c9b49f515c756cef13d72dcdcc3ef12993874c407a 1
 
< 0.1%
0481fc919b38f1572d4ba04b069766102d904a662ff0969f04266ebc48a9605d625c64356bd669d7bc1b778f5500a140812b58f19f2044372e50b31a01f030d4 1
 
< 0.1%
0500576055e3d46b0ed507761bfeae64457b1b58871f42b70f71fc5a464b4bf6364d2d8364bc90d5c093de919ecb3826bc6f1be7864b07998f3df9179f069040 1
 
< 0.1%
067575f551ac7df5aa918f14e1fb63daa10dcb0790d2a17af577d0b1ac6606d8506608e1f7d48c4a34fced54bd0b93591cfdce6a0cae43d23d58fcf195b68ffb 1
 
< 0.1%
07acb1d484e4c78c22307e80ec0daa51cb4f40a049985638c185c765d516a666790f66a9ac4dee8b480c84eb9f0ec2b5486eb3f9897abdde0b411eadce9d41d0 1
 
< 0.1%
Other values (746488) 746488
> 99.9%
2023-11-18T13:42:16.740676image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
2 5977028
 
6.3%
b 5976194
 
6.3%
d 5976170
 
6.3%
f 5974807
 
6.3%
c 5973904
 
6.3%
9 5973029
 
6.3%
4 5972549
 
6.3%
0 5971903
 
6.2%
8 5971379
 
6.2%
7 5971307
 
6.2%
Other values (6) 35813474
37.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 95551744
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
2 5977028
 
6.3%
b 5976194
 
6.3%
d 5976170
 
6.3%
f 5974807
 
6.3%
c 5973904
 
6.3%
9 5973029
 
6.3%
4 5972549
 
6.3%
0 5971903
 
6.2%
8 5971379
 
6.2%
7 5971307
 
6.2%
Other values (6) 35813474
37.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 95551744
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
2 5977028
 
6.3%
b 5976194
 
6.3%
d 5976170
 
6.3%
f 5974807
 
6.3%
c 5973904
 
6.3%
9 5973029
 
6.3%
4 5972549
 
6.3%
0 5971903
 
6.2%
8 5971379
 
6.2%
7 5971307
 
6.2%
Other values (6) 35813474
37.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 95551744
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
2 5977028
 
6.3%
b 5976194
 
6.3%
d 5976170
 
6.3%
f 5974807
 
6.3%
c 5973904
 
6.3%
9 5973029
 
6.3%
4 5972549
 
6.3%
0 5971903
 
6.2%
8 5971379
 
6.2%
7 5971307
 
6.2%
Other values (6) 35813474
37.5%

RD_NO
Text

Distinct742191
Distinct (%)100.0%
Missing4307
Missing (%)0.6%
Memory size5.7 MiB
2023-11-18T13:42:17.760060image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Length

Max length8
Median length8
Mean length8
Min length8

Characters and Unicode

Total characters5937528
Distinct characters36
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique742191 ?
Unique (%)100.0%

Sample

1st rowJG341943
2nd rowJG338388
3rd rowJG350008
4th rowJG338049
5th rowJG338431
ValueCountFrequency (%)
ja358029 1
 
< 0.1%
jg338185 1
 
< 0.1%
jg338437 1
 
< 0.1%
jg340852 1
 
< 0.1%
jg337673 1
 
< 0.1%
jg350008 1
 
< 0.1%
jg338049 1
 
< 0.1%
jg338431 1
 
< 0.1%
jf345041 1
 
< 0.1%
jg337491 1
 
< 0.1%
Other values (742181) 742181
> 99.9%
2023-11-18T13:42:19.376874image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
J 688106
11.6%
2 550762
9.3%
1 550701
9.3%
3 536317
9.0%
4 533628
9.0%
5 448197
 
7.5%
0 375291
 
6.3%
6 371574
 
6.3%
7 365614
 
6.2%
8 362035
 
6.1%
Other values (26) 1155303
19.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 5937528
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
J 688106
11.6%
2 550762
9.3%
1 550701
9.3%
3 536317
9.0%
4 533628
9.0%
5 448197
 
7.5%
0 375291
 
6.3%
6 371574
 
6.3%
7 365614
 
6.2%
8 362035
 
6.1%
Other values (26) 1155303
19.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 5937528
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
J 688106
11.6%
2 550762
9.3%
1 550701
9.3%
3 536317
9.0%
4 533628
9.0%
5 448197
 
7.5%
0 375291
 
6.3%
6 371574
 
6.3%
7 365614
 
6.2%
8 362035
 
6.1%
Other values (26) 1155303
19.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 5937528
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
J 688106
11.6%
2 550762
9.3%
1 550701
9.3%
3 536317
9.0%
4 533628
9.0%
5 448197
 
7.5%
0 375291
 
6.3%
6 371574
 
6.3%
7 365614
 
6.2%
8 362035
 
6.1%
Other values (26) 1155303
19.5%

CRASH_DATE_EST_I
Text

MISSING 

Distinct2
Distinct (%)< 0.1%
Missing690109
Missing (%)92.4%
Memory size5.7 MiB
2023-11-18T13:42:19.533970image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters56389
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowN
2nd rowY
3rd rowY
4th rowY
5th rowY
ValueCountFrequency (%)
y 49160
87.2%
n 7229
 
12.8%
2023-11-18T13:42:19.988888image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
Y 49160
87.2%
N 7229
 
12.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 56389
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
Y 49160
87.2%
N 7229
 
12.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 56389
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
Y 49160
87.2%
N 7229
 
12.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 56389
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
Y 49160
87.2%
N 7229
 
12.8%
Distinct488837
Distinct (%)65.5%
Missing0
Missing (%)0.0%
Memory size5.7 MiB
2023-11-18T13:42:20.396371image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Length

Max length22
Median length22
Mean length22
Min length22

Characters and Unicode

Total characters16422956
Distinct characters16
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique350823 ?
Unique (%)47.0%

Sample

1st row07/12/2023 03:05:00 PM
2nd row07/12/2023 05:50:00 PM
3rd row07/12/2023 02:00:00 PM
4th row07/12/2023 07:05:00 AM
5th row07/12/2023 06:30:00 PM
ValueCountFrequency (%)
pm 483395
 
21.6%
am 263103
 
11.7%
03:00:00 13172
 
0.6%
08:00:00 13051
 
0.6%
02:00:00 12785
 
0.6%
04:00:00 12738
 
0.6%
09:00:00 12681
 
0.6%
01:00:00 12581
 
0.6%
06:00:00 12284
 
0.5%
05:00:00 12234
 
0.5%
Other values (3664) 1391470
62.1%
2023-11-18T13:42:20.992738image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 4469707
27.2%
2 1891284
11.5%
1 1547159
 
9.4%
/ 1492996
 
9.1%
1492996
 
9.1%
: 1492996
 
9.1%
M 746498
 
4.5%
P 483395
 
2.9%
3 482148
 
2.9%
5 470294
 
2.9%
Other values (6) 1853483
11.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 16422956
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 4469707
27.2%
2 1891284
11.5%
1 1547159
 
9.4%
/ 1492996
 
9.1%
1492996
 
9.1%
: 1492996
 
9.1%
M 746498
 
4.5%
P 483395
 
2.9%
3 482148
 
2.9%
5 470294
 
2.9%
Other values (6) 1853483
11.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 16422956
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 4469707
27.2%
2 1891284
11.5%
1 1547159
 
9.4%
/ 1492996
 
9.1%
1492996
 
9.1%
: 1492996
 
9.1%
M 746498
 
4.5%
P 483395
 
2.9%
3 482148
 
2.9%
5 470294
 
2.9%
Other values (6) 1853483
11.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 16422956
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 4469707
27.2%
2 1891284
11.5%
1 1547159
 
9.4%
/ 1492996
 
9.1%
1492996
 
9.1%
: 1492996
 
9.1%
M 746498
 
4.5%
P 483395
 
2.9%
3 482148
 
2.9%
5 470294
 
2.9%
Other values (6) 1853483
11.3%

POSTED_SPEED_LIMIT
Real number (ℝ)

Distinct45
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean28.38453017
Minimum0
Maximum99
Zeros7326
Zeros (%)1.0%
Negative0
Negative (%)0.0%
Memory size5.7 MiB
2023-11-18T13:42:21.275281image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile15
Q130
median30
Q330
95-th percentile35
Maximum99
Range99
Interquartile range (IQR)0

Descriptive statistics

Standard deviation6.218632767
Coefficient of variation (CV)0.219085281
Kurtosis7.714664511
Mean28.38453017
Median Absolute Deviation (MAD)0
Skewness-1.832373858
Sum21188995
Variance38.6713935
MonotonicityNot monotonic
2023-11-18T13:42:21.604697image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=45)
ValueCountFrequency (%)
30 548985
73.5%
35 50079
 
6.7%
25 46962
 
6.3%
20 31146
 
4.2%
15 26490
 
3.5%
10 17319
 
2.3%
0 7326
 
1.0%
40 7187
 
1.0%
45 4895
 
0.7%
5 4485
 
0.6%
Other values (35) 1624
 
0.2%
ValueCountFrequency (%)
0 7326
1.0%
1 39
 
< 0.1%
2 26
 
< 0.1%
3 177
 
< 0.1%
4 2
 
< 0.1%
ValueCountFrequency (%)
99 66
< 0.1%
70 4
 
< 0.1%
65 18
 
< 0.1%
63 1
 
< 0.1%
62 1
 
< 0.1%
Distinct19
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size5.7 MiB
2023-11-18T13:42:21.902852image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Length

Max length24
Median length11
Mean length12.26585604
Min length5

Characters and Unicode

Total characters9156437
Distinct characters25
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNO CONTROLS
2nd rowNO CONTROLS
3rd rowNO CONTROLS
4th rowTRAFFIC SIGNAL
5th rowNO CONTROLS
ValueCountFrequency (%)
no 426374
29.2%
controls 426333
29.2%
signal 207025
14.2%
traffic 206740
14.1%
stop 73884
 
5.1%
sign/flasher 73884
 
5.1%
unknown 28365
 
1.9%
other 6635
 
0.5%
sign 2003
 
0.1%
crossing 1240
 
0.1%
Other values (18) 9629
 
0.7%
2023-11-18T13:42:22.514322image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
O 1391776
15.2%
N 1227532
13.4%
S 862103
9.4%
R 720051
7.9%
715614
7.8%
T 715103
7.8%
L 711880
7.8%
C 635174
6.9%
I 495818
 
5.4%
A 494088
 
5.4%
Other values (15) 1187298
13.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 9156437
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
O 1391776
15.2%
N 1227532
13.4%
S 862103
9.4%
R 720051
7.9%
715614
7.8%
T 715103
7.8%
L 711880
7.8%
C 635174
6.9%
I 495818
 
5.4%
A 494088
 
5.4%
Other values (15) 1187298
13.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 9156437
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
O 1391776
15.2%
N 1227532
13.4%
S 862103
9.4%
R 720051
7.9%
715614
7.8%
T 715103
7.8%
L 711880
7.8%
C 635174
6.9%
I 495818
 
5.4%
A 494088
 
5.4%
Other values (15) 1187298
13.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 9156437
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
O 1391776
15.2%
N 1227532
13.4%
S 862103
9.4%
R 720051
7.9%
715614
7.8%
T 715103
7.8%
L 711880
7.8%
C 635174
6.9%
I 495818
 
5.4%
A 494088
 
5.4%
Other values (15) 1187298
13.0%
Distinct8
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size5.7 MiB
2023-11-18T13:42:22.749974image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Length

Max length24
Median length11
Mean length13.86156025
Min length5

Characters and Unicode

Total characters10347627
Distinct characters21
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNO CONTROLS
2nd rowNO CONTROLS
3rd rowNO CONTROLS
4th rowUNKNOWN
5th rowNO CONTROLS
ValueCountFrequency (%)
no 431347
29.9%
controls 431347
29.9%
functioning 262137
18.2%
properly 256328
17.8%
unknown 46914
 
3.3%
other 5743
 
0.4%
improperly 3559
 
0.2%
not 2250
 
0.2%
worn 270
 
< 0.1%
reflective 270
 
< 0.1%
Other values (2) 357
 
< 0.1%
2023-11-18T13:42:23.252148image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
O 1871242
18.1%
N 1792454
17.3%
R 957674
9.3%
T 702017
 
6.8%
694024
 
6.7%
C 693754
 
6.7%
L 691774
 
6.7%
I 528547
 
5.1%
P 519774
 
5.0%
S 431521
 
4.2%
Other values (11) 1464846
14.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 10347627
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
O 1871242
18.1%
N 1792454
17.3%
R 957674
9.3%
T 702017
 
6.8%
694024
 
6.7%
C 693754
 
6.7%
L 691774
 
6.7%
I 528547
 
5.1%
P 519774
 
5.0%
S 431521
 
4.2%
Other values (11) 1464846
14.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 10347627
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
O 1871242
18.1%
N 1792454
17.3%
R 957674
9.3%
T 702017
 
6.8%
694024
 
6.7%
C 693754
 
6.7%
L 691774
 
6.7%
I 528547
 
5.1%
P 519774
 
5.0%
S 431521
 
4.2%
Other values (11) 1464846
14.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 10347627
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
O 1871242
18.1%
N 1792454
17.3%
R 957674
9.3%
T 702017
 
6.8%
694024
 
6.7%
C 693754
 
6.7%
L 691774
 
6.7%
I 528547
 
5.1%
P 519774
 
5.0%
S 431521
 
4.2%
Other values (11) 1464846
14.2%
Distinct12
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size5.7 MiB
2023-11-18T13:42:23.456129image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Length

Max length24
Median length5
Mean length5.334319717
Min length4

Characters and Unicode

Total characters3982059
Distinct characters25
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowCLEAR
2nd rowCLEAR
3rd rowCLEAR
4th rowFREEZING RAIN/DRIZZLE
5th rowCLEAR
ValueCountFrequency (%)
clear 588571
78.6%
rain 64056
 
8.6%
unknown 39154
 
5.2%
snow 26830
 
3.6%
cloudy/overcast 21994
 
2.9%
other 2338
 
0.3%
freezing 1368
 
0.2%
rain/drizzle 1368
 
0.2%
fog/smoke/haze 1100
 
0.1%
sleet/hail 934
 
0.1%
Other values (8) 1004
 
0.1%
2023-11-18T13:42:24.098878image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
R 681363
17.1%
A 678176
17.0%
C 632706
15.9%
E 621663
15.6%
L 614205
15.4%
N 211635
 
5.3%
O 115061
 
2.9%
I 69651
 
1.7%
W 66529
 
1.7%
U 61148
 
1.5%
Other values (15) 229922
 
5.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 3982059
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
R 681363
17.1%
A 678176
17.0%
C 632706
15.9%
E 621663
15.6%
L 614205
15.4%
N 211635
 
5.3%
O 115061
 
2.9%
I 69651
 
1.7%
W 66529
 
1.7%
U 61148
 
1.5%
Other values (15) 229922
 
5.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 3982059
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
R 681363
17.1%
A 678176
17.0%
C 632706
15.9%
E 621663
15.6%
L 614205
15.4%
N 211635
 
5.3%
O 115061
 
2.9%
I 69651
 
1.7%
W 66529
 
1.7%
U 61148
 
1.5%
Other values (15) 229922
 
5.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 3982059
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
R 681363
17.1%
A 678176
17.0%
C 632706
15.9%
E 621663
15.6%
L 614205
15.4%
N 211635
 
5.3%
O 115061
 
2.9%
I 69651
 
1.7%
W 66529
 
1.7%
U 61148
 
1.5%
Other values (15) 229922
 
5.8%
Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size5.7 MiB
2023-11-18T13:42:24.334511image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Length

Max length22
Median length8
Mean length10.84172362
Min length4

Characters and Unicode

Total characters8093325
Distinct characters18
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowDAYLIGHT
2nd rowDAYLIGHT
3rd rowDAYLIGHT
4th rowDAYLIGHT
5th rowDAYLIGHT
ValueCountFrequency (%)
daylight 481127
44.8%
darkness 198824
18.5%
lighted 163554
 
15.2%
road 163554
 
15.2%
unknown 32591
 
3.0%
dusk 21535
 
2.0%
dawn 12421
 
1.2%
2023-11-18T13:42:24.883546image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
D 1041015
12.9%
A 855926
10.6%
L 644681
 
8.0%
I 644681
 
8.0%
G 644681
 
8.0%
H 644681
 
8.0%
T 644681
 
8.0%
Y 481127
 
5.9%
S 419183
 
5.2%
R 362378
 
4.5%
Other values (8) 1710291
21.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 8093325
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
D 1041015
12.9%
A 855926
10.6%
L 644681
 
8.0%
I 644681
 
8.0%
G 644681
 
8.0%
H 644681
 
8.0%
T 644681
 
8.0%
Y 481127
 
5.9%
S 419183
 
5.2%
R 362378
 
4.5%
Other values (8) 1710291
21.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 8093325
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
D 1041015
12.9%
A 855926
10.6%
L 644681
 
8.0%
I 644681
 
8.0%
G 644681
 
8.0%
H 644681
 
8.0%
T 644681
 
8.0%
Y 481127
 
5.9%
S 419183
 
5.2%
R 362378
 
4.5%
Other values (8) 1710291
21.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 8093325
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
D 1041015
12.9%
A 855926
10.6%
L 644681
 
8.0%
I 644681
 
8.0%
G 644681
 
8.0%
H 644681
 
8.0%
T 644681
 
8.0%
Y 481127
 
5.9%
S 419183
 
5.2%
R 362378
 
4.5%
Other values (8) 1710291
21.1%
Distinct18
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size5.7 MiB
2023-11-18T13:42:25.125788image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Length

Max length28
Median length20
Mean length13.47939311
Min length5

Characters and Unicode

Total characters10062340
Distinct characters25
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowTURNING
2nd rowREAR END
3rd rowOTHER OBJECT
4th rowREAR END
5th rowREAR END
ValueCountFrequency (%)
rear 182379
11.5%
parked 174012
11.0%
motor 174012
11.0%
vehicle 174012
11.0%
end 168802
10.6%
sideswipe 124122
7.8%
direction 124122
7.8%
same 113530
7.2%
turning 105951
6.7%
angle 80764
5.1%
Other values (15) 165658
10.4%
2023-11-18T13:42:25.636454image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
E 1594852
15.8%
R 977527
9.7%
I 859867
 
8.5%
840866
 
8.4%
D 665328
 
6.6%
N 624103
 
6.2%
A 586332
 
5.8%
O 578845
 
5.8%
T 514799
 
5.1%
S 406937
 
4.0%
Other values (15) 2412884
24.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 10062340
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
E 1594852
15.8%
R 977527
9.7%
I 859867
 
8.5%
840866
 
8.4%
D 665328
 
6.6%
N 624103
 
6.2%
A 586332
 
5.8%
O 578845
 
5.8%
T 514799
 
5.1%
S 406937
 
4.0%
Other values (15) 2412884
24.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 10062340
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
E 1594852
15.8%
R 977527
9.7%
I 859867
 
8.5%
840866
 
8.4%
D 665328
 
6.6%
N 624103
 
6.2%
A 586332
 
5.8%
O 578845
 
5.8%
T 514799
 
5.1%
S 406937
 
4.0%
Other values (15) 2412884
24.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 10062340
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
E 1594852
15.8%
R 977527
9.7%
I 859867
 
8.5%
840866
 
8.4%
D 665328
 
6.6%
N 624103
 
6.2%
A 586332
 
5.8%
O 578845
 
5.8%
T 514799
 
5.1%
S 406937
 
4.0%
Other values (15) 2412884
24.0%
Distinct20
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size5.7 MiB
2023-11-18T13:42:25.887333image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Length

Max length31
Median length11
Mean length14.21355181
Min length4

Characters and Unicode

Total characters10610388
Distinct characters28
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNOT DIVIDED
2nd rowNOT DIVIDED
3rd rowNOT DIVIDED
4th rowNOT DIVIDED
5th rowNOT DIVIDED
ValueCountFrequency (%)
divided 490907
27.3%
not 448291
24.9%
164301
 
9.1%
w/median 164301
 
9.1%
raised 121221
 
6.7%
one-way 96101
 
5.3%
parking 51176
 
2.8%
lot 51176
 
2.8%
barrier 43080
 
2.4%
four 42368
 
2.4%
Other values (22) 127111
 
7.1%
2023-11-18T13:42:26.577886image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
D 1761368
16.6%
I 1390350
13.1%
1053535
9.9%
E 996805
9.4%
N 832853
7.8%
O 685464
 
6.5%
T 568569
 
5.4%
A 541866
 
5.1%
V 494318
 
4.7%
R 396783
 
3.7%
Other values (18) 1888477
17.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 10610388
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
D 1761368
16.6%
I 1390350
13.1%
1053535
9.9%
E 996805
9.4%
N 832853
7.8%
O 685464
 
6.5%
T 568569
 
5.4%
A 541866
 
5.1%
V 494318
 
4.7%
R 396783
 
3.7%
Other values (18) 1888477
17.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 10610388
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
D 1761368
16.6%
I 1390350
13.1%
1053535
9.9%
E 996805
9.4%
N 832853
7.8%
O 685464
 
6.5%
T 568569
 
5.4%
A 541866
 
5.1%
V 494318
 
4.7%
R 396783
 
3.7%
Other values (18) 1888477
17.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 10610388
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
D 1761368
16.6%
I 1390350
13.1%
1053535
9.9%
E 996805
9.4%
N 832853
7.8%
O 685464
 
6.5%
T 568569
 
5.4%
A 541866
 
5.1%
V 494318
 
4.7%
R 396783
 
3.7%
Other values (18) 1888477
17.8%

LANE_CNT
Real number (ℝ)

MISSING  SKEWED  ZEROS 

Distinct41
Distinct (%)< 0.1%
Missing547494
Missing (%)73.3%
Infinite0
Infinite (%)0.0%
Mean13.33043054
Minimum0
Maximum1191625
Zeros8032
Zeros (%)1.1%
Negative0
Negative (%)0.0%
Memory size5.7 MiB
2023-11-18T13:42:26.970202image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q12
median2
Q34
95-th percentile4
Maximum1191625
Range1191625
Interquartile range (IQR)2

Descriptive statistics

Standard deviation2961.638357
Coefficient of variation (CV)222.1712455
Kurtosis134675.2081
Mean13.33043054
Median Absolute Deviation (MAD)1
Skewness350.2991961
Sum2652809
Variance8771301.756
MonotonicityNot monotonic
2023-11-18T13:42:27.346397image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=41)
ValueCountFrequency (%)
2 91152
 
12.2%
4 49588
 
6.6%
1 32547
 
4.4%
3 8674
 
1.2%
0 8032
 
1.1%
6 4502
 
0.6%
5 1940
 
0.3%
8 1908
 
0.3%
7 184
 
< 0.1%
10 162
 
< 0.1%
Other values (31) 315
 
< 0.1%
(Missing) 547494
73.3%
ValueCountFrequency (%)
0 8032
 
1.1%
1 32547
 
4.4%
2 91152
12.2%
3 8674
 
1.2%
4 49588
6.6%
ValueCountFrequency (%)
1191625 1
< 0.1%
433634 1
< 0.1%
299679 1
< 0.1%
218474 1
< 0.1%
902 1
< 0.1%
Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size5.7 MiB
2023-11-18T13:42:27.707052image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Length

Max length21
Median length18
Mean length17.9454667
Min length12

Characters and Unicode

Total characters13396255
Distinct characters17
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSTRAIGHT AND LEVEL
2nd rowSTRAIGHT AND LEVEL
3rd rowSTRAIGHT AND LEVEL
4th rowSTRAIGHT AND LEVEL
5th rowSTRAIGHT AND LEVEL
ValueCountFrequency (%)
straight 739573
33.1%
level 733463
32.8%
and 727998
32.6%
on 13035
 
0.6%
grade 10661
 
0.5%
curve 6925
 
0.3%
hillcrest 2374
 
0.1%
2023-11-18T13:42:28.333038image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1487531
11.1%
E 1486886
11.1%
T 1481520
11.1%
A 1478232
11.0%
L 1471674
11.0%
R 759533
 
5.7%
G 750234
 
5.6%
S 741947
 
5.5%
I 741947
 
5.5%
H 741947
 
5.5%
Other values (7) 2254804
16.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 13396255
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1487531
11.1%
E 1486886
11.1%
T 1481520
11.1%
A 1478232
11.0%
L 1471674
11.0%
R 759533
 
5.7%
G 750234
 
5.6%
S 741947
 
5.5%
I 741947
 
5.5%
H 741947
 
5.5%
Other values (7) 2254804
16.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 13396255
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1487531
11.1%
E 1486886
11.1%
T 1481520
11.1%
A 1478232
11.0%
L 1471674
11.0%
R 759533
 
5.7%
G 750234
 
5.6%
S 741947
 
5.5%
I 741947
 
5.5%
H 741947
 
5.5%
Other values (7) 2254804
16.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 13396255
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1487531
11.1%
E 1486886
11.1%
T 1481520
11.1%
A 1478232
11.0%
L 1471674
11.0%
R 759533
 
5.7%
G 750234
 
5.6%
S 741947
 
5.5%
I 741947
 
5.5%
H 741947
 
5.5%
Other values (7) 2254804
16.8%
Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size5.7 MiB
2023-11-18T13:42:28.883268image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Length

Max length15
Median length3
Mean length3.68478951
Min length3

Characters and Unicode

Total characters2750688
Distinct characters19
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowUNKNOWN
2nd rowDRY
3rd rowDRY
4th rowUNKNOWN
5th rowWET
ValueCountFrequency (%)
dry 554470
69.3%
wet 98336
 
12.3%
unknown 59857
 
7.5%
snow 26458
 
3.3%
or 26458
 
3.3%
slush 26458
 
3.3%
ice 5214
 
0.7%
other 1877
 
0.2%
sand 286
 
< 0.1%
mud 286
 
< 0.1%
2023-11-18T13:42:29.369767image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
R 583091
21.2%
D 555328
20.2%
Y 554470
20.2%
N 206315
 
7.5%
W 184651
 
6.7%
O 114650
 
4.2%
E 105427
 
3.8%
T 100499
 
3.7%
U 86601
 
3.1%
S 79660
 
2.9%
Other values (9) 179996
 
6.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2750688
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
R 583091
21.2%
D 555328
20.2%
Y 554470
20.2%
N 206315
 
7.5%
W 184651
 
6.7%
O 114650
 
4.2%
E 105427
 
3.8%
T 100499
 
3.7%
U 86601
 
3.1%
S 79660
 
2.9%
Other values (9) 179996
 
6.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2750688
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
R 583091
21.2%
D 555328
20.2%
Y 554470
20.2%
N 206315
 
7.5%
W 184651
 
6.7%
O 114650
 
4.2%
E 105427
 
3.8%
T 100499
 
3.7%
U 86601
 
3.1%
S 79660
 
2.9%
Other values (9) 179996
 
6.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2750688
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
R 583091
21.2%
D 555328
20.2%
Y 554470
20.2%
N 206315
 
7.5%
W 184651
 
6.7%
O 114650
 
4.2%
E 105427
 
3.8%
T 100499
 
3.7%
U 86601
 
3.1%
S 79660
 
2.9%
Other values (9) 179996
 
6.5%
Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size5.7 MiB
2023-11-18T13:42:29.573729image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Length

Max length17
Median length10
Mean length9.490290664
Min length5

Characters and Unicode

Total characters7084483
Distinct characters20
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNO DEFECTS
2nd rowNO DEFECTS
3rd rowRUT, HOLES
4th rowUNKNOWN
5th rowNO DEFECTS
ValueCountFrequency (%)
no 605786
44.4%
defects 605786
44.4%
unknown 125684
 
9.2%
rut 5842
 
0.4%
holes 5842
 
0.4%
other 4133
 
0.3%
worn 3069
 
0.2%
surface 3069
 
0.2%
shoulder 1403
 
0.1%
defect 1403
 
0.1%
Other values (3) 1743
 
0.1%
2023-11-18T13:42:30.060199image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
E 1229406
17.4%
N 986488
13.9%
O 747079
10.5%
617262
8.7%
T 617164
8.7%
S 616681
8.7%
F 610258
8.6%
C 610258
8.6%
D 609754
8.6%
U 135998
 
1.9%
Other values (10) 304135
 
4.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 7084483
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
E 1229406
17.4%
N 986488
13.9%
O 747079
10.5%
617262
8.7%
T 617164
8.7%
S 616681
8.7%
F 610258
8.6%
C 610258
8.6%
D 609754
8.6%
U 135998
 
1.9%
Other values (10) 304135
 
4.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 7084483
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
E 1229406
17.4%
N 986488
13.9%
O 747079
10.5%
617262
8.7%
T 617164
8.7%
S 616681
8.7%
F 610258
8.6%
C 610258
8.6%
D 609754
8.6%
U 135998
 
1.9%
Other values (10) 304135
 
4.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 7084483
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
E 1229406
17.4%
N 986488
13.9%
O 747079
10.5%
617262
8.7%
T 617164
8.7%
S 616681
8.7%
F 610258
8.6%
C 610258
8.6%
D 609754
8.6%
U 135998
 
1.9%
Other values (10) 304135
 
4.3%

REPORT_TYPE
Text

MISSING 

Distinct3
Distinct (%)< 0.1%
Missing21222
Missing (%)2.8%
Memory size5.7 MiB
2023-11-18T13:42:30.337177image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Length

Max length26
Median length26
Mean length18.20682333
Min length7

Characters and Unicode

Total characters13204972
Distinct characters15
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNOT ON SCENE (DESK REPORT)
2nd rowNOT ON SCENE (DESK REPORT)
3rd rowNOT ON SCENE (DESK REPORT)
4th rowNOT ON SCENE (DESK REPORT)
5th rowNOT ON SCENE (DESK REPORT)
ValueCountFrequency (%)
on 725036
27.0%
scene 725036
27.0%
not 411278
15.3%
desk 411278
15.3%
report 411278
15.3%
amended 240
 
< 0.1%
2023-11-18T13:42:30.876061image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
E 2273108
17.2%
1958870
14.8%
N 1861590
14.1%
O 1547592
11.7%
S 1136314
8.6%
T 822556
 
6.2%
R 822556
 
6.2%
C 725036
 
5.5%
D 411758
 
3.1%
( 411278
 
3.1%
Other values (5) 1234314
9.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 13204972
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
E 2273108
17.2%
1958870
14.8%
N 1861590
14.1%
O 1547592
11.7%
S 1136314
8.6%
T 822556
 
6.2%
R 822556
 
6.2%
C 725036
 
5.5%
D 411758
 
3.1%
( 411278
 
3.1%
Other values (5) 1234314
9.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 13204972
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
E 2273108
17.2%
1958870
14.8%
N 1861590
14.1%
O 1547592
11.7%
S 1136314
8.6%
T 822556
 
6.2%
R 822556
 
6.2%
C 725036
 
5.5%
D 411758
 
3.1%
( 411278
 
3.1%
Other values (5) 1234314
9.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 13204972
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
E 2273108
17.2%
1958870
14.8%
N 1861590
14.1%
O 1547592
11.7%
S 1136314
8.6%
T 822556
 
6.2%
R 822556
 
6.2%
C 725036
 
5.5%
D 411758
 
3.1%
( 411278
 
3.1%
Other values (5) 1234314
9.3%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size5.7 MiB
2023-11-18T13:42:31.079504image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Length

Max length32
Median length22
Mean length24.64689256
Min length22

Characters and Unicode

Total characters18398856
Distinct characters18
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNO INJURY / DRIVE AWAY
2nd rowNO INJURY / DRIVE AWAY
3rd rowNO INJURY / DRIVE AWAY
4th rowNO INJURY / DRIVE AWAY
5th rowNO INJURY / DRIVE AWAY
ValueCountFrequency (%)
injury 746498
17.3%
746498
17.3%
no 548908
12.7%
drive 548908
12.7%
away 548908
12.7%
and 197590
 
4.6%
or 197590
 
4.6%
tow 197590
 
4.6%
due 197590
 
4.6%
to 197590
 
4.6%
2023-11-18T13:42:31.565983image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
3578762
19.5%
R 1690586
9.2%
N 1492996
 
8.1%
A 1492996
 
8.1%
Y 1295406
 
7.0%
I 1295406
 
7.0%
O 1141678
 
6.2%
D 944088
 
5.1%
U 944088
 
5.1%
/ 746498
 
4.1%
Other values (8) 3776352
20.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 18398856
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
3578762
19.5%
R 1690586
9.2%
N 1492996
 
8.1%
A 1492996
 
8.1%
Y 1295406
 
7.0%
I 1295406
 
7.0%
O 1141678
 
6.2%
D 944088
 
5.1%
U 944088
 
5.1%
/ 746498
 
4.1%
Other values (8) 3776352
20.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 18398856
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
3578762
19.5%
R 1690586
9.2%
N 1492996
 
8.1%
A 1492996
 
8.1%
Y 1295406
 
7.0%
I 1295406
 
7.0%
O 1141678
 
6.2%
D 944088
 
5.1%
U 944088
 
5.1%
/ 746498
 
4.1%
Other values (8) 3776352
20.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 18398856
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
3578762
19.5%
R 1690586
9.2%
N 1492996
 
8.1%
A 1492996
 
8.1%
Y 1295406
 
7.0%
I 1295406
 
7.0%
O 1141678
 
6.2%
D 944088
 
5.1%
U 944088
 
5.1%
/ 746498
 
4.1%
Other values (8) 3776352
20.5%
Distinct2
Distinct (%)< 0.1%
Missing575368
Missing (%)77.1%
Memory size5.7 MiB
2023-11-18T13:42:31.707030image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters171130
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowY
2nd rowY
3rd rowY
4th rowY
5th rowY
ValueCountFrequency (%)
y 163089
95.3%
n 8041
 
4.7%
2023-11-18T13:42:32.099353image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
Y 163089
95.3%
N 8041
 
4.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 171130
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
Y 163089
95.3%
N 8041
 
4.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 171130
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
Y 163089
95.3%
N 8041
 
4.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 171130
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
Y 163089
95.3%
N 8041
 
4.7%

NOT_RIGHT_OF_WAY_I
Text

MISSING 

Distinct2
Distinct (%)< 0.1%
Missing711724
Missing (%)95.3%
Memory size5.7 MiB
2023-11-18T13:42:32.272092image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters34774
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowY
2nd rowY
3rd rowN
4th rowY
5th rowY
ValueCountFrequency (%)
y 31645
91.0%
n 3129
 
9.0%
2023-11-18T13:42:32.773696image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
Y 31645
91.0%
N 3129
 
9.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 34774
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
Y 31645
91.0%
N 3129
 
9.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 34774
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
Y 31645
91.0%
N 3129
 
9.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 34774
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
Y 31645
91.0%
N 3129
 
9.0%

HIT_AND_RUN_I
Text

MISSING 

Distinct2
Distinct (%)< 0.1%
Missing513706
Missing (%)68.8%
Memory size5.7 MiB
2023-11-18T13:42:32.930366image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters232792
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowY
2nd rowY
3rd rowY
4th rowY
5th rowY
ValueCountFrequency (%)
y 222769
95.7%
n 10023
 
4.3%
2023-11-18T13:42:33.322702image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
Y 222769
95.7%
N 10023
 
4.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 232792
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
Y 222769
95.7%
N 10023
 
4.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 232792
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
Y 222769
95.7%
N 10023
 
4.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 232792
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
Y 222769
95.7%
N 10023
 
4.3%

DAMAGE
Text

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size5.7 MiB
2023-11-18T13:42:33.511045image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Length

Max length13
Median length11
Mean length11.65843713
Min length11

Characters and Unicode

Total characters8703000
Distinct characters13
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowOVER $1,500
2nd row$501 - $1,500
3rd row$501 - $1,500
4th row$501 - $1,500
5th rowOVER $1,500
ValueCountFrequency (%)
1,500 659818
37.0%
over 457397
25.7%
501 202421
 
11.4%
202421
 
11.4%
500 86680
 
4.9%
or 86680
 
4.9%
less 86680
 
4.9%
2023-11-18T13:42:34.028795image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 1695417
19.5%
1035599
11.9%
$ 948919
10.9%
5 948919
10.9%
1 862239
9.9%
, 659818
 
7.6%
O 544077
 
6.3%
E 544077
 
6.3%
R 544077
 
6.3%
V 457397
 
5.3%
Other values (3) 462461
 
5.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 8703000
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 1695417
19.5%
1035599
11.9%
$ 948919
10.9%
5 948919
10.9%
1 862239
9.9%
, 659818
 
7.6%
O 544077
 
6.3%
E 544077
 
6.3%
R 544077
 
6.3%
V 457397
 
5.3%
Other values (3) 462461
 
5.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 8703000
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 1695417
19.5%
1035599
11.9%
$ 948919
10.9%
5 948919
10.9%
1 862239
9.9%
, 659818
 
7.6%
O 544077
 
6.3%
E 544077
 
6.3%
R 544077
 
6.3%
V 457397
 
5.3%
Other values (3) 462461
 
5.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 8703000
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 1695417
19.5%
1035599
11.9%
$ 948919
10.9%
5 948919
10.9%
1 862239
9.9%
, 659818
 
7.6%
O 544077
 
6.3%
E 544077
 
6.3%
R 544077
 
6.3%
V 457397
 
5.3%
Other values (3) 462461
 
5.3%
Distinct566789
Distinct (%)75.9%
Missing0
Missing (%)0.0%
Memory size5.7 MiB
2023-11-18T13:42:34.405502image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Length

Max length22
Median length22
Mean length22
Min length22

Characters and Unicode

Total characters16422956
Distinct characters16
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique445694 ?
Unique (%)59.7%

Sample

1st row07/15/2023 11:30:00 AM
2nd row07/12/2023 06:41:00 PM
3rd row07/21/2023 10:10:00 AM
4th row07/12/2023 02:18:00 PM
5th row07/12/2023 07:15:00 PM
ValueCountFrequency (%)
pm 511205
 
22.8%
am 235293
 
10.5%
04:00:00 8002
 
0.4%
05:00:00 7702
 
0.3%
10:00:00 7607
 
0.3%
09:00:00 7482
 
0.3%
03:00:00 7471
 
0.3%
06:00:00 7408
 
0.3%
11:00:00 7121
 
0.3%
02:00:00 7120
 
0.3%
Other values (3634) 1433083
64.0%
2023-11-18T13:42:35.095475image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 4319127
26.3%
2 1920914
11.7%
1 1583256
 
9.6%
/ 1492996
 
9.1%
1492996
 
9.1%
: 1492996
 
9.1%
M 746498
 
4.5%
P 511205
 
3.1%
5 481302
 
2.9%
3 474182
 
2.9%
Other values (6) 1907484
11.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 16422956
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 4319127
26.3%
2 1920914
11.7%
1 1583256
 
9.6%
/ 1492996
 
9.1%
1492996
 
9.1%
: 1492996
 
9.1%
M 746498
 
4.5%
P 511205
 
3.1%
5 481302
 
2.9%
3 474182
 
2.9%
Other values (6) 1907484
11.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 16422956
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 4319127
26.3%
2 1920914
11.7%
1 1583256
 
9.6%
/ 1492996
 
9.1%
1492996
 
9.1%
: 1492996
 
9.1%
M 746498
 
4.5%
P 511205
 
3.1%
5 481302
 
2.9%
3 474182
 
2.9%
Other values (6) 1907484
11.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 16422956
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 4319127
26.3%
2 1920914
11.7%
1 1583256
 
9.6%
/ 1492996
 
9.1%
1492996
 
9.1%
: 1492996
 
9.1%
M 746498
 
4.5%
P 511205
 
3.1%
5 481302
 
2.9%
3 474182
 
2.9%
Other values (6) 1907484
11.6%
Distinct40
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size5.7 MiB
2023-11-18T13:42:35.362352image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Length

Max length80
Median length75
Mean length23.72158934
Min length6

Characters and Unicode

Total characters17708119
Distinct characters33
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowIMPROPER TURNING/NO SIGNAL
2nd rowFOLLOWING TOO CLOSELY
3rd rowNOT APPLICABLE
4th rowFOLLOWING TOO CLOSELY
5th rowFOLLOWING TOO CLOSELY
ValueCountFrequency (%)
to 435084
17.7%
unable 288466
 
11.7%
determine 288466
 
11.7%
improper 118400
 
4.8%
failing 113294
 
4.6%
yield 81956
 
3.3%
right-of-way 81696
 
3.3%
closely 73697
 
3.0%
too 73697
 
3.0%
following 73697
 
3.0%
Other values (106) 828451
33.7%
2023-11-18T13:42:35.958654image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
E 2168452
12.2%
1710406
 
9.7%
I 1497878
 
8.5%
N 1333515
 
7.5%
O 1290521
 
7.3%
T 1127005
 
6.4%
L 1080178
 
6.1%
R 1022074
 
5.8%
A 975290
 
5.5%
G 658719
 
3.7%
Other values (23) 4844081
27.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 17708119
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
E 2168452
12.2%
1710406
 
9.7%
I 1497878
 
8.5%
N 1333515
 
7.5%
O 1290521
 
7.3%
T 1127005
 
6.4%
L 1080178
 
6.1%
R 1022074
 
5.8%
A 975290
 
5.5%
G 658719
 
3.7%
Other values (23) 4844081
27.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 17708119
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
E 2168452
12.2%
1710406
 
9.7%
I 1497878
 
8.5%
N 1333515
 
7.5%
O 1290521
 
7.3%
T 1127005
 
6.4%
L 1080178
 
6.1%
R 1022074
 
5.8%
A 975290
 
5.5%
G 658719
 
3.7%
Other values (23) 4844081
27.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 17708119
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
E 2168452
12.2%
1710406
 
9.7%
I 1497878
 
8.5%
N 1333515
 
7.5%
O 1290521
 
7.3%
T 1127005
 
6.4%
L 1080178
 
6.1%
R 1022074
 
5.8%
A 975290
 
5.5%
G 658719
 
3.7%
Other values (23) 4844081
27.4%
Distinct40
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size5.7 MiB
2023-11-18T13:42:36.303711image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Length

Max length80
Median length75
Mean length19.50897256
Min length6

Characters and Unicode

Total characters14563409
Distinct characters33
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowUNABLE TO DETERMINE
2nd rowNOT APPLICABLE
3rd rowNOT APPLICABLE
4th rowUNABLE TO DETERMINE
5th rowFOLLOWING TOO CLOSELY
ValueCountFrequency (%)
to 351413
16.8%
not 305917
14.6%
applicable 304977
14.6%
unable 270813
12.9%
determine 270813
12.9%
failing 51434
 
2.5%
improper 35490
 
1.7%
speed 31330
 
1.5%
reduce 28419
 
1.4%
avoid 28419
 
1.4%
Other values (106) 415290
19.8%
2023-11-18T13:42:37.119629image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
E 1917492
13.2%
1347817
 
9.3%
L 1164862
 
8.0%
N 1155817
 
7.9%
A 1142774
 
7.8%
I 1072834
 
7.4%
T 1057755
 
7.3%
O 944144
 
6.5%
P 758644
 
5.2%
R 592968
 
4.1%
Other values (23) 3408302
23.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 14563409
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
E 1917492
13.2%
1347817
 
9.3%
L 1164862
 
8.0%
N 1155817
 
7.9%
A 1142774
 
7.8%
I 1072834
 
7.4%
T 1057755
 
7.3%
O 944144
 
6.5%
P 758644
 
5.2%
R 592968
 
4.1%
Other values (23) 3408302
23.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 14563409
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
E 1917492
13.2%
1347817
 
9.3%
L 1164862
 
8.0%
N 1155817
 
7.9%
A 1142774
 
7.8%
I 1072834
 
7.4%
T 1057755
 
7.3%
O 944144
 
6.5%
P 758644
 
5.2%
R 592968
 
4.1%
Other values (23) 3408302
23.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 14563409
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
E 1917492
13.2%
1347817
 
9.3%
L 1164862
 
8.0%
N 1155817
 
7.9%
A 1142774
 
7.8%
I 1072834
 
7.4%
T 1057755
 
7.3%
O 944144
 
6.5%
P 758644
 
5.2%
R 592968
 
4.1%
Other values (23) 3408302
23.4%

STREET_NO
Real number (ℝ)

Distinct11621
Distinct (%)1.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3685.153833
Minimum0
Maximum451100
Zeros4
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size5.7 MiB
2023-11-18T13:42:37.496348image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile149
Q11241
median3200
Q35600
95-th percentile9012
Maximum451100
Range451100
Interquartile range (IQR)4359

Descriptive statistics

Standard deviation2891.772465
Coefficient of variation (CV)0.784708752
Kurtosis767.4773845
Mean3685.153833
Median Absolute Deviation (MAD)2103
Skewness5.679196651
Sum2750959966
Variance8362347.99
MonotonicityNot monotonic
2023-11-18T13:42:37.810156image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1600 4831
 
0.6%
100 4542
 
0.6%
800 4376
 
0.6%
200 4202
 
0.6%
300 3817
 
0.5%
2400 3659
 
0.5%
4700 3626
 
0.5%
1200 3623
 
0.5%
500 3612
 
0.5%
6300 3533
 
0.5%
Other values (11611) 706677
94.7%
ValueCountFrequency (%)
0 4
 
< 0.1%
1 3079
0.4%
2 1512
0.2%
3 601
 
0.1%
4 133
 
< 0.1%
ValueCountFrequency (%)
451100 1
 
< 0.1%
34453 1
 
< 0.1%
13799 5
< 0.1%
13787 1
 
< 0.1%
13781 1
 
< 0.1%
Distinct4
Distinct (%)< 0.1%
Missing4
Missing (%)< 0.1%
Memory size5.7 MiB
2023-11-18T13:42:37.982891image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters746494
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowW
2nd rowS
3rd rowS
4th rowN
5th rowN
ValueCountFrequency (%)
w 266471
35.7%
s 250567
33.6%
n 178172
23.9%
e 51284
 
6.9%
2023-11-18T13:42:38.469359image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
W 266471
35.7%
S 250567
33.6%
N 178172
23.9%
E 51284
 
6.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 746494
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
W 266471
35.7%
S 250567
33.6%
N 178172
23.9%
E 51284
 
6.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 746494
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
W 266471
35.7%
S 250567
33.6%
N 178172
23.9%
E 51284
 
6.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 746494
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
W 266471
35.7%
S 250567
33.6%
N 178172
23.9%
E 51284
 
6.9%
Distinct1627
Distinct (%)0.2%
Missing1
Missing (%)< 0.1%
Memory size5.7 MiB
2023-11-18T13:42:38.830023image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Length

Max length31
Median length24
Mean length10.67744679
Min length4

Characters and Unicode

Total characters7970682
Distinct characters40
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique142 ?
Unique (%)< 0.1%

Sample

1st row63RD ST
2nd rowPULASKI RD
3rd rowSTONY ISLAND AVE
4th rowASHLAND AVE
5th rowELSTON AVE
ValueCountFrequency (%)
ave 377954
23.7%
st 232469
 
14.6%
rd 47923
 
3.0%
dr 45707
 
2.9%
blvd 28517
 
1.8%
lake 23758
 
1.5%
western 21673
 
1.4%
shore 19937
 
1.2%
pulaski 17792
 
1.1%
cicero 16724
 
1.0%
Other values (1330) 763271
47.8%
2023-11-18T13:42:39.567242image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
E 912681
11.5%
A 869561
10.9%
849229
 
10.7%
T 566991
 
7.1%
S 526536
 
6.6%
R 510169
 
6.4%
V 461726
 
5.8%
N 400227
 
5.0%
L 372028
 
4.7%
O 333705
 
4.2%
Other values (30) 2167829
27.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 7970682
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
E 912681
11.5%
A 869561
10.9%
849229
 
10.7%
T 566991
 
7.1%
S 526536
 
6.6%
R 510169
 
6.4%
V 461726
 
5.8%
N 400227
 
5.0%
L 372028
 
4.7%
O 333705
 
4.2%
Other values (30) 2167829
27.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 7970682
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
E 912681
11.5%
A 869561
10.9%
849229
 
10.7%
T 566991
 
7.1%
S 526536
 
6.6%
R 510169
 
6.4%
V 461726
 
5.8%
N 400227
 
5.0%
L 372028
 
4.7%
O 333705
 
4.2%
Other values (30) 2167829
27.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 7970682
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
E 912681
11.5%
A 869561
10.9%
849229
 
10.7%
T 566991
 
7.1%
S 526536
 
6.6%
R 510169
 
6.4%
V 461726
 
5.8%
N 400227
 
5.0%
L 372028
 
4.7%
O 333705
 
4.2%
Other values (30) 2167829
27.2%

BEAT_OF_OCCURRENCE
Real number (ℝ)

Distinct276
Distinct (%)< 0.1%
Missing5
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean1241.510779
Minimum111
Maximum6100
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.7 MiB
2023-11-18T13:42:40.147513image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum111
5-th percentile131
Q1713
median1211
Q31822
95-th percentile2513
Maximum6100
Range5989
Interquartile range (IQR)1109

Descriptive statistics

Standard deviation705.4060095
Coefficient of variation (CV)0.5681835562
Kurtosis-0.9988632594
Mean1241.510779
Median Absolute Deviation (MAD)579
Skewness0.1817459565
Sum926779106
Variance497597.6383
MonotonicityNot monotonic
2023-11-18T13:42:40.492963image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1834 9184
 
1.2%
114 7658
 
1.0%
813 7425
 
1.0%
815 7027
 
0.9%
1831 7027
 
0.9%
122 6677
 
0.9%
833 6136
 
0.8%
834 5655
 
0.8%
2413 5510
 
0.7%
2512 5461
 
0.7%
Other values (266) 678733
90.9%
ValueCountFrequency (%)
111 3275
0.4%
112 2298
 
0.3%
113 1744
 
0.2%
114 7658
1.0%
121 3496
0.5%
ValueCountFrequency (%)
6100 3
 
< 0.1%
2535 2329
0.3%
2534 3135
0.4%
2533 5224
0.7%
2532 2121
0.3%

PHOTOS_TAKEN_I
Text

MISSING 

Distinct2
Distinct (%)< 0.1%
Missing737007
Missing (%)98.7%
Memory size5.7 MiB
2023-11-18T13:42:40.743832image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters9491
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowY
2nd rowY
3rd rowN
4th rowN
5th rowY
ValueCountFrequency (%)
y 7221
76.1%
n 2270
 
23.9%
2023-11-18T13:42:41.261476image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
Y 7221
76.1%
N 2270
 
23.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 9491
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
Y 7221
76.1%
N 2270
 
23.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 9491
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
Y 7221
76.1%
N 2270
 
23.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 9491
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
Y 7221
76.1%
N 2270
 
23.9%

STATEMENTS_TAKEN_I
Text

MISSING 

Distinct2
Distinct (%)< 0.1%
Missing730402
Missing (%)97.8%
Memory size5.7 MiB
2023-11-18T13:42:41.433832image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters16096
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowY
2nd rowY
3rd rowY
4th rowY
5th rowY
ValueCountFrequency (%)
y 13130
81.6%
n 2966
 
18.4%
2023-11-18T13:42:42.045842image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
Y 13130
81.6%
N 2966
 
18.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 16096
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
Y 13130
81.6%
N 2966
 
18.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 16096
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
Y 13130
81.6%
N 2966
 
18.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 16096
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
Y 13130
81.6%
N 2966
 
18.4%

DOORING_I
Text

MISSING 

Distinct2
Distinct (%)0.1%
Missing744212
Missing (%)99.7%
Memory size5.7 MiB
2023-11-18T13:42:42.187320image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters2286
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowN
2nd rowY
3rd rowY
4th rowY
5th rowY
ValueCountFrequency (%)
y 1549
67.8%
n 737
32.2%
2023-11-18T13:42:42.564590image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
Y 1549
67.8%
N 737
32.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2286
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
Y 1549
67.8%
N 737
32.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2286
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
Y 1549
67.8%
N 737
32.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2286
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
Y 1549
67.8%
N 737
32.2%

WORK_ZONE_I
Text

MISSING 

Distinct2
Distinct (%)< 0.1%
Missing742175
Missing (%)99.4%
Memory size5.7 MiB
2023-11-18T13:42:42.705060image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters4323
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowN
2nd rowY
3rd rowY
4th rowY
5th rowY
ValueCountFrequency (%)
y 3370
78.0%
n 953
 
22.0%
2023-11-18T13:42:43.112498image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
Y 3370
78.0%
N 953
 
22.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4323
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
Y 3370
78.0%
N 953
 
22.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4323
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
Y 3370
78.0%
N 953
 
22.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4323
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
Y 3370
78.0%
N 953
 
22.0%

WORK_ZONE_TYPE
Text

MISSING 

Distinct4
Distinct (%)0.1%
Missing743128
Missing (%)99.5%
Memory size5.7 MiB
2023-11-18T13:42:43.347709image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Length

Max length12
Median length12
Mean length10.89554896
Min length7

Characters and Unicode

Total characters36718
Distinct characters15
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowCONSTRUCTION
2nd rowUNKNOWN
3rd rowCONSTRUCTION
4th rowCONSTRUCTION
5th rowCONSTRUCTION
ValueCountFrequency (%)
construction 2352
69.8%
unknown 466
 
13.8%
maintenance 342
 
10.1%
utility 210
 
6.2%
2023-11-18T13:42:43.834200image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
N 7128
19.4%
T 5466
14.9%
O 5170
14.1%
C 5046
13.7%
I 3114
8.5%
U 3028
8.2%
S 2352
 
6.4%
R 2352
 
6.4%
A 684
 
1.9%
E 684
 
1.9%
Other values (5) 1694
 
4.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 36718
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
N 7128
19.4%
T 5466
14.9%
O 5170
14.1%
C 5046
13.7%
I 3114
8.5%
U 3028
8.2%
S 2352
 
6.4%
R 2352
 
6.4%
A 684
 
1.9%
E 684
 
1.9%
Other values (5) 1694
 
4.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 36718
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
N 7128
19.4%
T 5466
14.9%
O 5170
14.1%
C 5046
13.7%
I 3114
8.5%
U 3028
8.2%
S 2352
 
6.4%
R 2352
 
6.4%
A 684
 
1.9%
E 684
 
1.9%
Other values (5) 1694
 
4.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 36718
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
N 7128
19.4%
T 5466
14.9%
O 5170
14.1%
C 5046
13.7%
I 3114
8.5%
U 3028
8.2%
S 2352
 
6.4%
R 2352
 
6.4%
A 684
 
1.9%
E 684
 
1.9%
Other values (5) 1694
 
4.6%

WORKERS_PRESENT_I
Text

MISSING 

Distinct2
Distinct (%)0.2%
Missing745383
Missing (%)99.9%
Memory size5.7 MiB
2023-11-18T13:42:43.975665image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1115
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowY
2nd rowN
3rd rowY
4th rowY
5th rowY
ValueCountFrequency (%)
y 990
88.8%
n 125
 
11.2%
2023-11-18T13:42:44.383629image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
Y 990
88.8%
N 125
 
11.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1115
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
Y 990
88.8%
N 125
 
11.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1115
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
Y 990
88.8%
N 125
 
11.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1115
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
Y 990
88.8%
N 125
 
11.2%

NUM_UNITS
Real number (ℝ)

Distinct17
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.034546643
Minimum1
Maximum18
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.7 MiB
2023-11-18T13:42:44.603251image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median2
Q32
95-th percentile3
Maximum18
Range17
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.4527458705
Coefficient of variation (CV)0.2225291182
Kurtosis37.15778299
Mean2.034546643
Median Absolute Deviation (MAD)0
Skewness3.293458877
Sum1518785
Variance0.2049788233
MonotonicityNot monotonic
2023-11-18T13:42:44.838477image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
ValueCountFrequency (%)
2 652897
87.5%
1 41506
 
5.6%
3 41265
 
5.5%
4 8011
 
1.1%
5 1922
 
0.3%
6 562
 
0.1%
7 184
 
< 0.1%
8 82
 
< 0.1%
9 33
 
< 0.1%
10 16
 
< 0.1%
Other values (7) 20
 
< 0.1%
ValueCountFrequency (%)
1 41506
 
5.6%
2 652897
87.5%
3 41265
 
5.5%
4 8011
 
1.1%
5 1922
 
0.3%
ValueCountFrequency (%)
18 3
< 0.1%
16 1
 
< 0.1%
15 1
 
< 0.1%
14 2
< 0.1%
13 1
 
< 0.1%
Distinct5
Distinct (%)< 0.1%
Missing1630
Missing (%)0.2%
Memory size5.7 MiB
2023-11-18T13:42:45.057585image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Length

Max length24
Median length23
Mean length22.94108486
Min length5

Characters and Unicode

Total characters17088080
Distinct characters19
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNO INDICATION OF INJURY
2nd rowNO INDICATION OF INJURY
3rd rowNO INDICATION OF INJURY
4th rowNO INDICATION OF INJURY
5th rowNO INDICATION OF INJURY
ValueCountFrequency (%)
injury 713496
25.4%
no 643460
22.9%
indication 643460
22.9%
of 643460
22.9%
nonincapacitating 57308
 
2.0%
reported 30560
 
1.1%
not 30560
 
1.1%
evident 30560
 
1.1%
incapacitating 12728
 
0.5%
fatal 812
 
< 0.1%
2023-11-18T13:42:45.591390image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
N 2959684
17.3%
I 2884544
16.9%
2061536
12.1%
O 2048808
12.0%
T 876024
 
5.1%
A 855192
 
5.0%
C 783532
 
4.6%
R 774616
 
4.5%
U 713496
 
4.2%
Y 713496
 
4.2%
Other values (9) 2417152
14.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 17088080
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
N 2959684
17.3%
I 2884544
16.9%
2061536
12.1%
O 2048808
12.0%
T 876024
 
5.1%
A 855192
 
5.0%
C 783532
 
4.6%
R 774616
 
4.5%
U 713496
 
4.2%
Y 713496
 
4.2%
Other values (9) 2417152
14.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 17088080
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
N 2959684
17.3%
I 2884544
16.9%
2061536
12.1%
O 2048808
12.0%
T 876024
 
5.1%
A 855192
 
5.0%
C 783532
 
4.6%
R 774616
 
4.5%
U 713496
 
4.2%
Y 713496
 
4.2%
Other values (9) 2417152
14.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 17088080
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
N 2959684
17.3%
I 2884544
16.9%
2061536
12.1%
O 2048808
12.0%
T 876024
 
5.1%
A 855192
 
5.0%
C 783532
 
4.6%
R 774616
 
4.5%
U 713496
 
4.2%
Y 713496
 
4.2%
Other values (9) 2417152
14.1%

INJURIES_TOTAL
Real number (ℝ)

ZEROS 

Distinct20
Distinct (%)< 0.1%
Missing1619
Missing (%)0.2%
Infinite0
Infinite (%)0.0%
Mean0.1872049017
Minimum0
Maximum21
Zeros643471
Zeros (%)86.2%
Negative0
Negative (%)0.0%
Memory size5.7 MiB
2023-11-18T13:42:45.826617image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1
Maximum21
Range21
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.5616714036
Coefficient of variation (CV)3.000302868
Kurtosis45.7304038
Mean0.1872049017
Median Absolute Deviation (MAD)0
Skewness4.853102547
Sum139445
Variance0.3154747656
MonotonicityNot monotonic
2023-11-18T13:42:46.093533image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=20)
ValueCountFrequency (%)
0 643471
86.2%
1 76447
 
10.2%
2 16893
 
2.3%
3 5134
 
0.7%
4 1836
 
0.2%
5 642
 
0.1%
6 257
 
< 0.1%
7 96
 
< 0.1%
8 41
 
< 0.1%
9 22
 
< 0.1%
Other values (10) 40
 
< 0.1%
(Missing) 1619
 
0.2%
ValueCountFrequency (%)
0 643471
86.2%
1 76447
 
10.2%
2 16893
 
2.3%
3 5134
 
0.7%
4 1836
 
0.2%
ValueCountFrequency (%)
21 3
< 0.1%
19 1
 
< 0.1%
17 1
 
< 0.1%
16 1
 
< 0.1%
15 7
< 0.1%

INJURIES_FATAL
Real number (ℝ)

SKEWED  ZEROS 

Distinct5
Distinct (%)< 0.1%
Missing1619
Missing (%)0.2%
Infinite0
Infinite (%)0.0%
Mean0.001184084932
Minimum0
Maximum4
Zeros744067
Zeros (%)99.7%
Negative0
Negative (%)0.0%
Memory size5.7 MiB
2023-11-18T13:42:46.344378image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum4
Range4
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.03741884207
Coefficient of variation (CV)31.60148488
Kurtosis1685.134693
Mean0.001184084932
Median Absolute Deviation (MAD)0
Skewness36.80343103
Sum882
Variance0.001400169742
MonotonicityNot monotonic
2023-11-18T13:42:46.595666image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=5)
ValueCountFrequency (%)
0 744067
99.7%
1 752
 
0.1%
2 51
 
< 0.1%
3 8
 
< 0.1%
4 1
 
< 0.1%
(Missing) 1619
 
0.2%
ValueCountFrequency (%)
0 744067
99.7%
1 752
 
0.1%
2 51
 
< 0.1%
3 8
 
< 0.1%
4 1
 
< 0.1%
ValueCountFrequency (%)
4 1
 
< 0.1%
3 8
 
< 0.1%
2 51
 
< 0.1%
1 752
 
0.1%
0 744067
99.7%

INJURIES_INCAPACITATING
Real number (ℝ)

ZEROS 

Distinct10
Distinct (%)< 0.1%
Missing1619
Missing (%)0.2%
Infinite0
Infinite (%)0.0%
Mean0.02011736134
Minimum0
Maximum10
Zeros732026
Zeros (%)98.1%
Negative0
Negative (%)0.0%
Memory size5.7 MiB
2023-11-18T13:42:46.877663image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum10
Range10
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.1662896112
Coefficient of variation (CV)8.26597526
Kurtosis193.4956569
Mean0.02011736134
Median Absolute Deviation (MAD)0
Skewness11.32033756
Sum14985
Variance0.02765223478
MonotonicityNot monotonic
2023-11-18T13:42:47.144161image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
0 732026
98.1%
1 11290
 
1.5%
2 1170
 
0.2%
3 267
 
< 0.1%
4 92
 
< 0.1%
5 25
 
< 0.1%
6 6
 
< 0.1%
7 1
 
< 0.1%
10 1
 
< 0.1%
8 1
 
< 0.1%
(Missing) 1619
 
0.2%
ValueCountFrequency (%)
0 732026
98.1%
1 11290
 
1.5%
2 1170
 
0.2%
3 267
 
< 0.1%
4 92
 
< 0.1%
ValueCountFrequency (%)
10 1
 
< 0.1%
8 1
 
< 0.1%
7 1
 
< 0.1%
6 6
 
< 0.1%
5 25
< 0.1%

INJURIES_NON_INCAPACITATING
Real number (ℝ)

ZEROS 

Distinct19
Distinct (%)< 0.1%
Missing1619
Missing (%)0.2%
Infinite0
Infinite (%)0.0%
Mean0.1057916789
Minimum0
Maximum21
Zeros685250
Zeros (%)91.8%
Negative0
Negative (%)0.0%
Memory size5.7 MiB
2023-11-18T13:42:47.395440image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1
Maximum21
Range21
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.4192080162
Coefficient of variation (CV)3.962580238
Kurtosis84.02611889
Mean0.1057916789
Median Absolute Deviation (MAD)0
Skewness6.443128886
Sum78802
Variance0.1757353608
MonotonicityNot monotonic
2023-11-18T13:42:47.646301image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=19)
ValueCountFrequency (%)
0 685250
91.8%
1 46826
 
6.3%
2 8815
 
1.2%
3 2574
 
0.3%
4 899
 
0.1%
5 307
 
< 0.1%
6 121
 
< 0.1%
7 41
 
< 0.1%
8 19
 
< 0.1%
10 7
 
< 0.1%
Other values (9) 20
 
< 0.1%
(Missing) 1619
 
0.2%
ValueCountFrequency (%)
0 685250
91.8%
1 46826
 
6.3%
2 8815
 
1.2%
3 2574
 
0.3%
4 899
 
0.1%
ValueCountFrequency (%)
21 2
< 0.1%
19 1
< 0.1%
18 1
< 0.1%
16 1
< 0.1%
15 1
< 0.1%

INJURIES_REPORTED_NOT_EVIDENT
Real number (ℝ)

ZEROS 

Distinct13
Distinct (%)< 0.1%
Missing1619
Missing (%)0.2%
Infinite0
Infinite (%)0.0%
Mean0.06011177654
Minimum0
Maximum15
Zeros710835
Zeros (%)95.2%
Negative0
Negative (%)0.0%
Memory size5.7 MiB
2023-11-18T13:42:47.881940image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum15
Range15
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.3148889908
Coefficient of variation (CV)5.238391026
Kurtosis99.17149089
Mean0.06011177654
Median Absolute Deviation (MAD)0
Skewness7.782077938
Sum44776
Variance0.09915507653
MonotonicityNot monotonic
2023-11-18T13:42:48.132802image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
0 710835
95.2%
1 26493
 
3.5%
2 5468
 
0.7%
3 1414
 
0.2%
4 432
 
0.1%
5 152
 
< 0.1%
6 40
 
< 0.1%
7 19
 
< 0.1%
9 9
 
< 0.1%
8 9
 
< 0.1%
Other values (3) 8
 
< 0.1%
(Missing) 1619
 
0.2%
ValueCountFrequency (%)
0 710835
95.2%
1 26493
 
3.5%
2 5468
 
0.7%
3 1414
 
0.2%
4 432
 
0.1%
ValueCountFrequency (%)
15 2
 
< 0.1%
11 1
 
< 0.1%
10 5
< 0.1%
9 9
< 0.1%
8 9
< 0.1%

INJURIES_NO_INDICATION
Real number (ℝ)

ZEROS 

Distinct46
Distinct (%)< 0.1%
Missing1619
Missing (%)0.2%
Infinite0
Infinite (%)0.0%
Mean2.005796915
Minimum0
Maximum61
Zeros15356
Zeros (%)2.1%
Negative0
Negative (%)0.0%
Memory size5.7 MiB
2023-11-18T13:42:48.415565image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q11
median2
Q32
95-th percentile4
Maximum61
Range61
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.15942532
Coefficient of variation (CV)0.5780372438
Kurtosis68.85038946
Mean2.005796915
Median Absolute Deviation (MAD)1
Skewness3.750287438
Sum1494076
Variance1.344267073
MonotonicityNot monotonic
2023-11-18T13:42:48.713460image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=46)
ValueCountFrequency (%)
2 345811
46.3%
1 228408
30.6%
3 94368
 
12.6%
4 35398
 
4.7%
0 15356
 
2.1%
5 14817
 
2.0%
6 6174
 
0.8%
7 2402
 
0.3%
8 1083
 
0.1%
9 446
 
0.1%
Other values (36) 616
 
0.1%
(Missing) 1619
 
0.2%
ValueCountFrequency (%)
0 15356
 
2.1%
1 228408
30.6%
2 345811
46.3%
3 94368
 
12.6%
4 35398
 
4.7%
ValueCountFrequency (%)
61 1
< 0.1%
50 1
< 0.1%
46 1
< 0.1%
45 2
< 0.1%
43 1
< 0.1%

INJURIES_UNKNOWN
Real number (ℝ)

CONSTANT  ZEROS 

Distinct1
Distinct (%)< 0.1%
Missing1619
Missing (%)0.2%
Infinite0
Infinite (%)0.0%
Mean0
Minimum0
Maximum0
Zeros744879
Zeros (%)99.8%
Negative0
Negative (%)0.0%
Memory size5.7 MiB
2023-11-18T13:42:49.215662image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum0
Range0
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0
Coefficient of variation (CV)nan
Kurtosis0
Mean0
Median Absolute Deviation (MAD)0
Skewness0
Sum0
Variance0
MonotonicityIncreasing
2023-11-18T13:42:49.608060image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=1)
ValueCountFrequency (%)
0 744879
99.8%
(Missing) 1619
 
0.2%
ValueCountFrequency (%)
0 744879
99.8%
ValueCountFrequency (%)
0 744879
99.8%

CRASH_HOUR
Real number (ℝ)

ZEROS 

Distinct24
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean13.2116362
Minimum0
Maximum23
Zeros16136
Zeros (%)2.2%
Negative0
Negative (%)0.0%
Memory size5.7 MiB
2023-11-18T13:42:49.937495image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2
Q19
median14
Q317
95-th percentile22
Maximum23
Range23
Interquartile range (IQR)8

Descriptive statistics

Standard deviation5.562006226
Coefficient of variation (CV)0.42099299
Kurtosis-0.3804707285
Mean13.2116362
Median Absolute Deviation (MAD)4
Skewness-0.4293979514
Sum9862460
Variance30.93591326
MonotonicityNot monotonic
2023-11-18T13:42:50.204472image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
15 57415
 
7.7%
16 57089
 
7.6%
17 55643
 
7.5%
14 50221
 
6.7%
18 45972
 
6.2%
13 45596
 
6.1%
12 43997
 
5.9%
8 39139
 
5.2%
11 38026
 
5.1%
9 34249
 
4.6%
Other values (14) 279151
37.4%
ValueCountFrequency (%)
0 16136
2.2%
1 13768
1.8%
2 11858
1.6%
3 9639
1.3%
4 8640
1.2%
ValueCountFrequency (%)
23 19454
2.6%
22 22462
3.0%
21 24472
3.3%
20 27253
3.7%
19 33821
4.5%

CRASH_DAY_OF_WEEK
Real number (ℝ)

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.124282985
Minimum1
Maximum7
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.7 MiB
2023-11-18T13:42:50.455229image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median4
Q36
95-th percentile7
Maximum7
Range6
Interquartile range (IQR)4

Descriptive statistics

Standard deviation1.980422656
Coefficient of variation (CV)0.4801859288
Kurtosis-1.239395844
Mean4.124282985
Median Absolute Deviation (MAD)2
Skewness-0.07838598747
Sum3078769
Variance3.922073896
MonotonicityNot monotonic
2023-11-18T13:42:50.722128image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
6 121469
16.3%
7 110645
14.8%
5 107297
14.4%
3 106208
14.2%
4 105585
14.1%
2 102697
13.8%
1 92597
12.4%
ValueCountFrequency (%)
1 92597
12.4%
2 102697
13.8%
3 106208
14.2%
4 105585
14.1%
5 107297
14.4%
ValueCountFrequency (%)
7 110645
14.8%
6 121469
16.3%
5 107297
14.4%
4 105585
14.1%
3 106208
14.2%

CRASH_MONTH
Real number (ℝ)

Distinct12
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.591970776
Minimum1
Maximum12
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.7 MiB
2023-11-18T13:42:50.983989image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q14
median7
Q310
95-th percentile12
Maximum12
Range11
Interquartile range (IQR)6

Descriptive statistics

Standard deviation3.395764421
Coefficient of variation (CV)0.5151364496
Kurtosis-1.167455286
Mean6.591970776
Median Absolute Deviation (MAD)3
Skewness-0.03593346066
Sum4920893
Variance11.53121601
MonotonicityNot monotonic
2023-11-18T13:42:51.224245image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
7 68279
9.1%
6 67527
9.0%
10 66703
8.9%
5 66513
8.9%
9 62801
8.4%
12 61713
8.3%
8 61124
8.2%
11 60487
8.1%
3 58885
7.9%
1 57832
7.7%
Other values (2) 114634
15.4%
ValueCountFrequency (%)
1 57832
7.7%
2 57477
7.7%
3 58885
7.9%
4 57157
7.7%
5 66513
8.9%
ValueCountFrequency (%)
12 61713
8.3%
11 60487
8.1%
10 66703
8.9%
9 62801
8.4%
8 61124
8.2%

LATITUDE
Real number (ℝ)

SKEWED 

Distinct283165
Distinct (%)38.2%
Missing4908
Missing (%)0.7%
Infinite0
Infinite (%)0.0%
Mean41.85470247
Minimum0
Maximum42.02277986
Zeros44
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size5.7 MiB
2023-11-18T13:42:51.491147image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile41.71273794
Q141.78202467
median41.87460252
Q341.92420756
95-th percentile41.99007835
Maximum42.02277986
Range42.02277986
Interquartile range (IQR)0.1421828885

Descriptive statistics

Standard deviation0.3337007713
Coefficient of variation (CV)0.007972838214
Kurtosis14680.89859
Mean41.85470247
Median Absolute Deviation (MAD)0.068278948
Skewness-117.0740476
Sum31039028.81
Variance0.1113562048
MonotonicityNot monotonic
2023-11-18T13:42:51.789309image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
41.97620114 1188
 
0.2%
41.90095892 682
 
0.1%
41.79142028 538
 
0.1%
41.7514606 521
 
0.1%
41.72225727 411
 
0.1%
41.75466012 356
 
< 0.1%
41.88085605 295
 
< 0.1%
41.78932932 289
 
< 0.1%
41.90075297 272
 
< 0.1%
41.89680497 271
 
< 0.1%
Other values (283155) 736767
98.7%
(Missing) 4908
 
0.7%
ValueCountFrequency (%)
0 44
< 0.1%
41.64467013 19
< 0.1%
41.64469152 4
 
< 0.1%
41.64469397 7
 
< 0.1%
41.64469408 1
 
< 0.1%
ValueCountFrequency (%)
42.02277986 9
< 0.1%
42.02275469 1
 
< 0.1%
42.02273632 1
 
< 0.1%
42.02272017 2
 
< 0.1%
42.02266893 1
 
< 0.1%

LONGITUDE
Real number (ℝ)

SKEWED 

Distinct283132
Distinct (%)38.2%
Missing4908
Missing (%)0.7%
Infinite0
Infinite (%)0.0%
Mean-87.673456
Minimum-87.93619295
Maximum0
Zeros44
Zeros (%)< 0.1%
Negative741546
Negative (%)99.3%
Memory size5.7 MiB
2023-11-18T13:42:52.401327image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum-87.93619295
5-th percentile-87.77679089
Q1-87.72161239
median-87.67390117
Q3-87.63313443
95-th percentile-87.58578342
Maximum0
Range87.93619295
Interquartile range (IQR)0.0884779585

Descriptive statistics

Standard deviation0.6779193971
Coefficient of variation (CV)-0.007732322051
Kurtosis16594.90134
Mean-87.673456
Median Absolute Deviation (MAD)0.043100507
Skewness128.3392678
Sum-65017758.23
Variance0.459574709
MonotonicityNot monotonic
2023-11-18T13:42:52.699506image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-87.90530913 1188
 
0.2%
-87.61992817 682
 
0.1%
-87.58014777 538
 
0.1%
-87.58597199 521
 
0.1%
-87.58527557 411
 
0.1%
-87.74138476 356
 
< 0.1%
-87.61763589 295
 
< 0.1%
-87.74164564 289
 
< 0.1%
-87.624235 272
 
< 0.1%
-87.61702742 271
 
< 0.1%
Other values (283122) 736767
98.7%
(Missing) 4908
 
0.7%
ValueCountFrequency (%)
-87.93619295 1
 
< 0.1%
-87.93587692 1
 
< 0.1%
-87.93476313 3
< 0.1%
-87.93450972 1
 
< 0.1%
-87.93401422 1
 
< 0.1%
ValueCountFrequency (%)
0 44
< 0.1%
-87.52458739 10
 
< 0.1%
-87.52458901 4
 
< 0.1%
-87.52464032 1
 
< 0.1%
-87.5246459 1
 
< 0.1%
Distinct283322
Distinct (%)38.2%
Missing4908
Missing (%)0.7%
Memory size5.7 MiB
2023-11-18T13:42:53.374016image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Length

Max length40
Median length40
Mean length39.77927022
Min length11

Characters and Unicode

Total characters29499909
Distinct characters20
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique182135 ?
Unique (%)24.6%

Sample

1st rowPOINT (-87.742064741348 41.778541938106)
2nd rowPOINT (-87.72182410033 41.742130554062)
3rd rowPOINT (-87.584789974824 41.719844228292)
4th rowPOINT (-87.668291181568 41.925104953308)
5th rowPOINT (-87.751990557158 41.97525809527)
ValueCountFrequency (%)
point 741590
33.3%
41.976201139024 1188
 
0.1%
87.905309125103 1188
 
0.1%
87.619928173678 682
 
< 0.1%
41.900958919109 682
 
< 0.1%
87.580147768689 538
 
< 0.1%
41.791420282098 538
 
< 0.1%
87.585971992965 521
 
< 0.1%
41.751460603167 521
 
< 0.1%
87.585275565077 411
 
< 0.1%
Other values (566634) 1476911
66.4%
2023-11-18T13:42:54.330452image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
7 2821015
 
9.6%
8 2641433
 
9.0%
4 2373176
 
8.0%
1 2323965
 
7.9%
6 2106851
 
7.1%
9 1869200
 
6.3%
5 1703071
 
5.8%
2 1664135
 
5.6%
3 1630744
 
5.5%
1483180
 
5.0%
Other values (10) 8883139
30.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 29499909
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
7 2821015
 
9.6%
8 2641433
 
9.0%
4 2373176
 
8.0%
1 2323965
 
7.9%
6 2106851
 
7.1%
9 1869200
 
6.3%
5 1703071
 
5.8%
2 1664135
 
5.6%
3 1630744
 
5.5%
1483180
 
5.0%
Other values (10) 8883139
30.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 29499909
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
7 2821015
 
9.6%
8 2641433
 
9.0%
4 2373176
 
8.0%
1 2323965
 
7.9%
6 2106851
 
7.1%
9 1869200
 
6.3%
5 1703071
 
5.8%
2 1664135
 
5.6%
3 1630744
 
5.5%
1483180
 
5.0%
Other values (10) 8883139
30.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 29499909
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
7 2821015
 
9.6%
8 2641433
 
9.0%
4 2373176
 
8.0%
1 2323965
 
7.9%
6 2106851
 
7.1%
9 1869200
 
6.3%
5 1703071
 
5.8%
2 1664135
 
5.6%
3 1630744
 
5.5%
1483180
 
5.0%
Other values (10) 8883139
30.1%